Access this on-demand session to understand how predictive analytics can help you identify students at risk of disengaging and intervene before they decide to leave. Student retention is a pressing issue for universities and HEIs, with non-completion affecting funding, revenue and institutional reputation. This session explains how data you already hold can be used to surface early warning signs and support more effective retention strategies.

Traditional reporting can help you understand why students leave after the event, but it rarely gives you the foresight needed to act in time. Predictive analytics changes this. By analysing patterns in student characteristics and behaviour, you can pinpoint high-risk groups or individuals while they are still enrolled and prioritise interventions that have the greatest chance of success.

The session cuts through the usual analytics hype and focuses on what works in practice. You will gain a clear understanding of how retention models are built, what data is useful, how to interpret risk indicators and how other UK institutions are already reducing churn through targeted, data-driven support. It is a practical, accessible introduction designed for teams who want to improve student outcomes without lengthy or expensive projects.

In just one hour you will learn:

• What predictive analytics is – an overview of common terminology and what it really means for you and your organisation.
• Best practice in retention analytics – how to ensure your predictive analytics project is successful
• How to build a timely student retention model – identifying high risk groups and individuals in a UK educational institution and understanding which preventative actions are most likely to be successful in each case
• How other UK HEIs are already reducing student churn using predictive analytics – real world case studies showcasing different approaches.